The present disclosure relates generally to the field of audio systems. More specifically, the present disclosure relates to noise reduction in an audio system.
Mobile voice applications, such as cellular phones, voice recognition systems, military radio applications and other single microphone devices, are prone to degradation from environmental noise. The quality of speech is deteriorated even further when these devices incorporate a low bit rate speech encoding algorithm that operates by modeling the vocal parameters of human speech and encoding them into packets of specific lengths. These packets are then transmitted over a desired radio channel using some designated type of modulation. On the receiving end the signal is demodulated, decoded, and the resulting reconstructed speech waveform is sent to an audio device where it is played. As a result, the magnitude and type of noise at the transmitting microphone can severely degrade the quality of speech generated by the model. Therefore, it has been discovered that the addition of a noise reduction algorithm before the speech encoding routine can greatly improve the quality of the reconstructed voice.
Many algorithms have been designed that attempt to improve the quality of speech communication by removing the effects of additive noise. A large number of these methods work in the frequency domain by calculating frequency specific attenuation parameters and applying them to respective discrete Fourier transform bins. However, the majority of these algorithms were developed under the assumption that speech is inherently present in every frequency region. Therefore, it has been shown that the quality can be improved if the spectral gain function utilizes a soft-decision attenuation parameter calculation based on the probability of speech presence. Many of these procedures excel at reducing the effects of stationary noise, but are challenged when confronted with nonstationary noise environments such as inside an airplane cockpit, a helicopter, a tank, another moving vehicle, or a noisy room.
Removing additive noise from a speech signal has numerous benefits (enhancement of the quality of mobile voice communications, improved speech recognition, etc). Over the years, many methods have been developed that attempt to remove noise from the signal. These methods range from spectral subtraction, Weiner filtering, maximum likelihood estimation (ML), minimum mean squared error (MMSE), subspace algorithms, and many others. In the end, the overall performance of all of these methods rests on an accurate estimate of the noise power spectral density. Specifically, noise overestimation can cause speech distortion, while underestimation can cause residual and musical noise. Some noise estimation techniques assume that the spectral characteristics of the noise change slowly with regards to the speech signal and attempt to estimate the noise during periods of speech pause.